Evaluating the Effect of Activity and Environment on Fall Risk in a Paradigm-Depending Laboratory Setting: Protocol for an Experimental Pilot Study
Notice bibliographique
Résumé
BACKGROUND: Knowledge about the causal factors leading to falls is still limited, and fall prevention interventions urgently need to be more effective to limit the otherwise increasing burden caused by falls in older people. To identify individual fall risk, it is important to understand the complex interplay of fall-related factors. Although fall events are common, they are seldom observed, and fall reports are often biased. Due to the rapid development of wearable inertial sensors, an objective approach to capture fall events and the corresponding circumstances is provided. OBJECTIVE: The aim of this work is to operationalize a prototypical dynamic fall risk model regarding 4 ecologically valid real-world scenarios (opening a door, slipping, tripping, and usage of public transportation). We hypothesize that individual fall risk is associated with an interplay of intrinsic risk factors, activity, and environmental factors that can be estimated by using data measured within a laboratory simulation setting. METHODS: We will recruit 30 community-dwelling people aged 60 years or older. To identify several fall-related intrinsic fall risk factors, appropriate clinical assessments will be selected. The experimental setup is adaptable so that the level of fall risk for each activity and each environmental factor is adjustable. By different levels of difficulty, the effect on the risk of falling will be investigated. An 8-camera motion tracking system will be used to record absolute body motions and limits of stability. All laboratory experiments will also be recorded by inertial sensors (L5, dominant leg) and video camera. Logistic regression analyses will be used to model the association between risk factors and falls. Continuous fall risk will be modeled by generalized linear regression models using margin of stability as outcome parameter. RESULTS: The results of this project will prove the concept and establish methods to further use the dynamic fall risk model. Recruitment and measurement initially began in October 2020 but were halted because of the COVID-19 pandemic. Recruitment and measurements recommenced in October 2022, and by February 2023, a total of 25 of the planned 30 subjects have been measured. CONCLUSIONS: In the field of fall prevention, a more precise fall risk model will have a significant impact on research leading to more effective prevention approaches. Given the described burden related to falls and the high prevalence, considerable improvements in fall prevention will have a significant impact on individual quality of life and also on society in general by reducing institutionalization and health care costs. The setup will enable the analysis of fall events and their circumstances ecologically valid in a laboratory setting and thereby will provide important information to estimate the individual instantaneous fall risk. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46930.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».